Face recognition in healthcare: key use cases

Face recognition in healthcare

Integration of technology in healthcare is an incredible transition that has changed numerous operations. Face recognition, machine learning, artificial intelligence, and the internet of things have become a daily business and talk in healthcare. These technologies have been implemented to deal with heavy workloads, poor healthcare outcomes, and workforce shortages. Technology has reduced the heavy burden on healthcare workers while transforming the lives of many patients and other community members.

Let’s take a look at how face recognition impacts healthcare.

What is face recognition?

Face recognition is a technology that helps identify an individual based on their specific facial landmarks. It calculates the similarity between human faces detected in video frames or digital images to find the best match. Facial recognition systems facilitate advanced video surveillance, human-computer interactions, access control, and more.

How does face recognition work?

Face recognition technology uses computer vision algorithms to select distinct details of individual faces. These details include the shape of the chin or distances between eyes, which are then translated into mathematical representations used to compare data on other faces found in the database.

Data concerning a certain face is called a face template. It is unique from photographs since it contains only specific details used to distinguish one face from another. Face recognition is accurate and can be used from a distance compared to other biometrics such as fingerprints.

Some systems calculate probability match scores rather than positively identifying images of an unknown person. Such systems calculate the match score between unknown individuals and face templates found in the database, thus offering ranked matches based on their likelihood of accurate identification.

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Key use cases in healthcare

Face recognition is applied in diverse areas in healthcare to facilitate numerous operations. Some of the key areas that utilize this technology include hospital security, diagnostics, and patient identification. Machine learning and neural networks are also being used in conjunction with face recognition to help in diverse medical research and other processes aimed at reducing suffering and saving patients’ lives.

  • Patient identification

Check-in and check-out processes are fundamental in healthcare setup. Hospitals use face recognition technology to scan patients’ identity and then run it through the hospital database, verifying people and minimizing issues of fraud, impersonation, etc. This helps eliminate patient errors and flawed procedures, minimize harm to the patient, and improve the overall patient experience.

  • Diagnosis

Face recognition has facilitated disease detection in simple and efficient ways. A smart mirror, for example, utilizes a phone or laptop camera to facilitate medical diagnosis. Telemedicine is currently being used to facilitate home care and personal checkups. Some apps utilize face recognition by enabling face scans to measure stress levels, heart rate, or blood pressure.

The technology is non-invasive and contact-free, thus offering a substantial advantage over other diagnostics. For example, children who might fear invasive procedures or healthcare setups can utilize this technology to monitor their health status.

Computer-aided diagnostics such as custom image recognition systems are also used in diagnostics with fewer errors. Application of object detection in deep learning and computer vision with face recognition helps in abnormality detection, facilitating proper treatment, rehabilitation, and quick recovery.

  • Pain and emotional detection

Some patients cannot express or describe feelings, making it hard for doctors to detect some mental conditions. Assessing infants or neonates may also be hard since not all of them cry due to pain. Finally, some gender or culture does not allow certain groups of people to cry; therefore, some people get stressed due to the fear of being criticized by others.

Facial recognition can be combined with emotion recognition technology to recognize certain emotions or pain and then analyze different events related to emotions of interest.

  • Therapy

Face recognition is serving as a doctor’s assistant by helping in the treatment process. Real-time habit detection helps in facilitating personalized care.  Face recognition can be used as an evidence-based therapy for diverse illnesses, such as mental disorders.

 

Future of face recognition in healthcare

Face recognition is ubiquitous in the current world and healthcare is no different. It ensures that healthcare providers access accurate patient data to avoid wrong diagnoses or treatment. The technology also minimizes and detects fraud or errors that interfere with patients’ confidentiality, trust, or outcomes. As a result, face recognition improves patients’ compliance and health outcomes.

Neural networks and machine learning algorithms are also used together with face recognition to facilitate the analysis of the collected data. Doctors and other healthcare providers use face recognition and other AI-related tools to ensure that patients get the best care. With all these tools, healthcare providers can detect pathologies and offer therapy to patients more effectively.

In the future, face recognition will be even more ubiquitous than it is today. It is likely to be applied to track automatic health changes and movement of patients globally, similarly to how automated license plate readers track motor vehicles using plate numbers.

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Melanie JohnsonThis is a guest article written by Melanie Johnson. Melanie Johnson is an AI and computer vision enthusiast with a wealth of experience in technical writing. She is passionate about innovation and AI-powered solutions, and loves sharing expert insights and educating individuals on tech.